Stacking classifiers for anti-spam filtering of e-mail (2001)
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| Venue: | Carnegie Mellon University |
| Citations: | 33 - 0 self |
BibTeX
@INPROCEEDINGS{Sakkis01stackingclassifiers,
author = {Georgios Sakkis and Ion Androutsopoulos and Georgios Paliouras and Vangelis Karkaletsis and Constantine D. Spyropoulos and Panagiotis Stamatopoulos},
title = {Stacking classifiers for anti-spam filtering of e-mail},
booktitle = {Carnegie Mellon University},
year = {2001},
pages = {44--50}
}
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Abstract
We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial email, or “spam”, floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in reallife applications.
